UNICEF Global Adolescent Mortality Story

Author

Prabhjas Singh (16815)

Introduction

Adolescence is a critical developmental stage, yet millions of adolescents continue to face preventable mortality risks worldwide.
This report explores adolescent mortality patterns globally, using data from UNICEF datasets.
We analyze how gender, geographic region, and economic development influence mortality outcomes.

Dataset Preparation

Code 
import pandas as pd
from plotnine import *
import plotly.express as px

# Load datasets
indicator = pd.read_csv('unicef_indicator_1_cleaned.csv')
metadata = pd.read_csv('unicef_metadata_cleaned.csv')

# Clean column names
indicator.columns = indicator.columns.str.strip().str.lower()
metadata.columns = metadata.columns.str.strip().str.lower()

# Merge datasets
data = pd.merge(indicator, metadata, on='country', how='left')

# Latest year
latest_year = data['time_period'].max()

Gender Differences in Mortality (Top 10 Countries)

Mortality risks differ across genders.
The chart below shows the top 10 countries with the highest adolescent mortality rates, comparing male and female adolescents.

Code 
# Filter latest year and gender
latest_data = data[data['time_period'] == latest_year]
latest_data = latest_data[latest_data['sex'].isin(['Male', 'Female'])]

# Top 10 countries
top10_countries = latest_data.groupby('country')['obs_value'].mean().sort_values(ascending=False).head(10).index.tolist()
top10_gender_data = latest_data[latest_data['country'].isin(top10_countries)]

# Grouped Bar Chart
(
    ggplot(top10_gender_data) +
    aes(x='country', y='obs_value', fill='sex') +
    geom_bar(stat='identity', position='dodge') +
    scale_fill_manual(values={"Male": "#1f77b4", "Female": "#e377c2"}) +
    theme_minimal() +
    labs(
        title='Top 10 Countries: Adolescent Mortality by Gender',
        x='Country',
        y='Mortality Rate (per 1,000)',
        fill='Gender'
    ) +
    theme(
        plot_title=element_text(size=18, face="bold"),
        axis_title_x=element_text(size=14),
        axis_title_y=element_text(size=14),
        axis_text_x=element_text(size=12, rotation=45, hjust=1),
        axis_text_y=element_text(size=12),
        legend_title=element_text(size=14),
        legend_text=element_text(size=12)
    )
)

Insight:
Males generally experience higher mortality than females, though the gap varies by country.
African nations dominate the top 10 list, highlighting regional disparities.

Global Mortality Distribution

The global map below visualizes adolescent mortality rates across countries.

Code 
# Fix country names
data['country'] = data['country'].str.title()

# Latest global data
latest_global_data = data[data['time_period'] == latest_year]

# World map
fig = px.choropleth(
    latest_global_data,
    locations="country",
    locationmode="country names",
    color="obs_value",
    color_continuous_scale="Blues",
    title="Global Distribution of Adolescent Mortality Rates",
    labels={'obs_value': 'Mortality Rate (per 1,000)'}
)

fig.show()

Insight:
Sub-Saharan Africa and parts of South Asia report the highest adolescent mortality rates, while Europe and North America report the lowest.

Mortality Patterns Across GDP Bands (Gender Split)

Grouping countries into GDP bands offers a clearer view of how economic development relates to adolescent mortality.

Code 
# Create GDP bands
bins = [0, 1000, 5000, 10000, 50000, 1000000]
labels = ['<1k', '1k-5k', '5k-10k', '10k-50k', '>50k']
data['gdp_band'] = pd.cut(data['gdp per capita (constant 2015 us$)'], bins=bins, labels=labels)

# Group and average
latest_gender_data = data[(data['time_period'] == latest_year) & (data['sex'].isin(['Male', 'Female']))]
avg_band_mortality = latest_gender_data.groupby(['gdp_band', 'sex'])['obs_value'].mean().reset_index()

# Scatterplot by GDP Band
(
    ggplot(avg_band_mortality) +
    aes(x='gdp_band', y='obs_value', color='sex') +
    geom_point(size=4) +
    geom_line(group='sex', size=1.5) +
    scale_color_manual(values={"Male": "#1f77b4", "Female": "#e377c2"}) +
    theme_minimal() +
    labs(
        title='Average Mortality Rate by GDP Band (Gender Split)',
        x='GDP Band',
        y='Average Mortality Rate (per 1,000)',
        color='Gender'
    ) +
    theme(
        plot_title=element_text(size=18, face="bold"),
        axis_title_x=element_text(size=14),
        axis_title_y=element_text(size=14),
        axis_text_x=element_text(size=12),
        axis_text_y=element_text(size=12),
        legend_title=element_text(size=14),
        legend_text=element_text(size=12)
    )
)

Insight:
Mortality rates fall sharply once GDP per capita exceeds $10,000, with gender differences narrowing substantially in wealthier countries.

Conclusion

This analysis reveals persistent inequalities in adolescent mortality outcomes across genders, geographies, and income groups.
Sub-Saharan Africa remains the most affected region, while high-income countries enjoy lower adolescent mortality risks.

Recommendations: - Prioritize healthcare investments in low- and middle-income countries. - Strengthen gender-specific adolescent health interventions. - Foster global partnerships to improve health equity.

Investing in adolescent health is critical for achieving a healthier and more sustainable future globally.